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vit-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask

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vit-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask
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---
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8420604512558536
    - name: Recall
      type: recall
      value: 0.8420604512558536
    - name: F1
      type: f1
      value: 0.840458775689156
    - name: Precision
      type: precision
      value: 0.8450034699086092
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3294
- Accuracy: 0.8421
- Recall: 0.8421
- F1: 0.8405
- Precision: 0.8450

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5269        | 0.9974 | 293  | 0.5393          | 0.8029   | 0.8029 | 0.7943 | 0.7941    |
| 0.4275        | 1.9983 | 587  | 0.4630          | 0.8182   | 0.8182 | 0.8103 | 0.8255    |
| 0.4681        | 2.9991 | 881  | 0.4346          | 0.8408   | 0.8408 | 0.8358 | 0.8557    |
| 0.3721        | 4.0    | 1175 | 0.3631          | 0.8450   | 0.8450 | 0.8417 | 0.8541    |
| 0.4054        | 4.9974 | 1468 | 0.3536          | 0.8455   | 0.8455 | 0.8445 | 0.8491    |
| 0.2519        | 5.9983 | 1762 | 0.3747          | 0.8421   | 0.8421 | 0.8391 | 0.8549    |
| 0.2923        | 6.9991 | 2056 | 0.3664          | 0.8395   | 0.8395 | 0.8402 | 0.8467    |
| 0.2288        | 8.0    | 2350 | 0.3496          | 0.8382   | 0.8382 | 0.8377 | 0.8442    |
| 0.1642        | 8.9974 | 2643 | 0.3455          | 0.8463   | 0.8463 | 0.8444 | 0.8468    |
| 0.1783        | 9.9745 | 2930 | 0.3468          | 0.8476   | 0.8476 | 0.8463 | 0.8490    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1